DETAILED ACTION
This examination is in response to the communication filed on 08/25/2026. Claims 1-20 are currently pending, wherein claims 1, 8 and 15 have been amended.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment/Arguments
Applicant's amendment to claims 1, 8, and 15 overcome the rejection under 35 U.S.C. 112(b) and therefore, the rejection has been withdrawn. In addition, the amendment overcomes the rejection under 35 U.S.C. §101 by reciting a practical application of the abstract idea, i.e., real-time adjustment of media processing during an online collaboration session based on the category of the identified issue, thus the rejection has been withdrawn.
The electronic Terminal Disclaimer filed on 08/25/2026 obviates the double patenting rejection.
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C. §103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cioffi et al. (US 2021/0314238 herein “Cioffi”) in view of Dalton et al. (US 11605384 herein “Dalton”), further in view of Komissarchick et al. (US 2017/0337923 herein “Komissarchick”), still further in view of Ellis et al. (US 2021/0091998 A1; herein “Ellis”).
Regarding claims 1, 9, and 17, Cioffi teaches a computer-implemented method ([0365] teaches disclosed methods may further relate to computer products for performing various computer-implemented operations)as recited in claim 1, an apparatus comprising a memory (¶[0364] teaches the system includes a non-transitory, tangible computer-readable medium such as ROM and RAM devices); a network interface configured to enable network communication (¶[0049] teaches the devices 210 may interface with the network access point 205 using a wireless or wired connection. In addition, ¶[0070] teaches the server 270 comprises a user interface 286 that supports bi-directional communication between the server 270 and the user/employee working from home, the employer's IT support group, the ISP(s), and/or the application provider) and a processor (¶[0403] teaches the system comprises one or more processors) as recited in claim 9; and one or more non-transitory computer readable storage media (¶[0364] teaches the discloses aspects may be encoded upon one or more non-transitory computer-readable media with instruction for one or more processors to cause steps to be performed) encoded with instructions that, when executed by a processor, cause the processor to execute a method as recited in claim 15, the method comprising:
detecting a phrase in an online collaboration session between a plurality of users, the phrase being by a first user to one or more second users (Fig. 3, element 390 “realtime User feedback” and ¶ [0153] teach real-time user QoE feedback 390 may include input from within a collaboration platform application such as a thumbs up/down or comments from chat/messaging streams running within the collaboration platform, i.e., streams may be monitored for comments on connection quality, or users make comments with specific keywords. In addition, ¶[0155] teaches real-time user QoE feedback data 390 can result from the immediate use of a chatbot or virtual assistant or even read the voice intonation to flag a real time application or service concern);
determining that the phrase indicates an issue with a quality of user experience of the online collaboration session (¶ [0148] teaches user QoE feedback may be real-time or delayed and may be direct or indirect. In addition, ¶ [0153] and [0154] teach real-time user QoE feedback data 390 may include comments from chat/messaging steams running within the collaboration platform where the steams may be monitored for comments on connection quality, or users make comments with specific keywords and different group members may express poor QoE my pressing “thumbs-down” on another participant’s communication because they cannot hear or see, which the sender may not realize. In such situations, other group members can indicate a problem with the thumbs-down indication, and the WFH application can try to correct this through profile change for the affected group member. The negative real-time QoE feedback of Cioffi is interpreted as indicating an issue with QoE);
labeling a log of metrics associated with the online collaboration session with a time stamp corresponding to a time and at least one of an indication of the phrase that was spoken (the “one of” language makes this element optional) or the category of the issue associated with the phrase that was spoken, to provide a labeled log of metrics that indicates a set of metric values associated with the online collaboration session at the time when the phrase as was spoken (under a broadest reasonable interpretation, the phrase “labeling a log of metrics” is interpreted as including storing real-time QoE event data/metrics for use in training machine learning models to identify and/or predict performance issues. This is supported by ¶ [014] of the instant application which states “the [media quality event] MQE data may be used to train machine learning devices to identify or predict imminent performance issues and perform actions with respect to the performance issue (e.g., log the performance issue, adjust parameters associated with online meetings to prevent issues from occurring etc. Cioffi ¶[0068] teaches the server 270 includes a metric generation apparatus 288 that generates an aggregated and/or plurality of different metrics applicable to the framework, including QoE metrics which all or some may be associated with a label or service category identifier. Further, ¶¶ [0372] and [0410] teach the input data includes timing data which provides time spans and/or time stamps associated with the QoS data and the QoE data. Thus, the QoE metrics include time stamps.);
determining one or more actions to perform based on the category of the issue associated with the phrase (¶[0077] teaches “In response to a learned ASC analysis, ASC improvement solutions address possible corrective actions, pro-active or re-active, that my dynamically tune or improve the internet connection’s tunable parameters. These action will lead to the connection’s “current state” or “profile,” which FIG. 3A also shows is an input to the learning process, being improved such the QoE of users on the network improves and associated productivity increases.”);
performing the one or more determined actions to improve the user experience based on detecting phrase, the one or more actions including transmitting the phrase or the category of the issue to an online collaboration session application associated with one or more users of the plurality of users, the online collaboration session application and automatically modifying at least one media processing parameter associated with the media processing adjustment while the online collaboration session is occurring (¶[0051] teaches the management system comprises a server 270 that is able to take multiple network measurements, improve performance by adjusting parameters, interact with one or more software agents located on device within the WFH architecture, and monitor network traffic across the diverse set of WHF users and connections that enable work collaboration. In addition, ¶ [0066] teaches the server 270 measures network connectivity, calculates metrics, manages and improves the network performance, and communicates with devices within the architecture. For example, ¶[0156] teaches the server 270 may repair the issue by improving the audio quality and video that is functioning poorly because of poor connectivity, and ¶[0219] teaches an application provider may adjust its audio and video compression schemes to match the user’s available throughput in real time. In addition, ¶[0060] teaches “The server 270 may also use an agent to a user interface (UI) to collect the user’s preferred service category and/or to collect user QoE feedback…may collect device information directly over an Internet connection to devices or indirectly gather such information from the gateway agent, or possibly also from the application server(s)” the audio and video compression schemes are interpreted as a media processing adjustment).
Cioffi fails to explicitly disclose that the phrase, e.g., real-time feedback comments, corresponds to a spoken phrase. Therefore, Cioffi fail to explicitly disclose “detecting a spoken phrase in an online collaboration session between a plurality of users, the phrase being spoken by a first user to one or more second users”, as recited in claims 1, 9, and 17.
Dalton teaches systems and methods for presenting interrupting content during human speech in conversational AI platforms that includes, inter alia,
detecting a spoken phrase in an online collaboration session between a plurality of users, the phrase being spoken by a first user to one or more second users (Fig. 6, element 610; and col. 21, lines 45-47 teaches in a first stage 610, user speech inputs 612 are received by the SFA which is configured to actively listen 614 to the user);
determining that the spoken phrase indicates an issue with a quality of user experience of the online collaboration session (Fig. 6, elements 640; FIGs 11A and 11B; and col. 21, lines 58-61 teaches the speech inputs are reviewed for indications of triggering event conditions and if a triggering event has occurred the event type may be identified. Dalton further teaches that a triggering event refers to an audible event or sequence of audible events that matches a pre-determined condition (see col. 3, lines 60-63). In addition, col. 24, lines 11-30 teaches a triggering event may be the mispronunciation of a caller’s name by a meeting leader. A mispronunciation of a caller’s name is interpreted as a quality of user experience issue for the caller who has indicated a preferred pronunciation of their name) ; and
performing one or more actions to improve the user experience based on detecting the spoken phrase (col. 24, lines 25-36 teaches in response to detection of the mispronunciation of the caller’s name by a meeting leader, the SFA cause the chatbot interface 1152 to present a corrective message 1154 ("Saira has indicated her name is to be pronounced "Sigh-rah" rather than Sara") to the meeting leader allowing the meeting leader to offer an apology 1114, thereby restoring the confidence in the first caller’s sense of inclusion and individuality without requiring her to correct her supervisor).
Cioffi and Dalton are analogous art because they are both directed to improving user experience during an online collaboration/meeting. One having ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to modify the real-time user QoE feedback functionality of Cioffi to include: detecting a spoken phrase; determining whether the detected spoken phrase indicates a QoE issue for a participant/caller; and in response to detecting such a triggering event performing an action to improve user QoE as taught by Dalton in order to offer moderating guidance and/or other timely feedback, thereby enriching the quality of the dialogue between the human participants (Dalton, col. 3, lines 44-46).
Although the combination of Cioffi and Dalton teaches the QoE metrics may be associated with a label or service category identifier (Dalton, ¶[0068]), the combination fails to explicitly disclose that the label is identified from a list of phrases, corresponding categories and attributes. Thus, the combination of Cioffi and Dalton fails to explicitly disclose identifying, from a list of phrases, corresponding categories of issues, and corresponding attributes, a category of the issue associated with the phrase and an attribute associated with the phrase.
Komissarichik teaches a system of creating voice-based dialog systems that provide more accurate and robust communications (Komissarichik, ¶[0001]). Paragraph [0030] of Komissarichik further teaches the system includes, inter alia, a “dialog nomenclature repository 13 [that] contains list of words and phrases that are used in voice dialogs between users and machine. The repository 13 can also contain different tags for words and phrases indicating categories and contexts they are used in. Contexts are interpreted as attributes.
The combination of Cioffi and Dalton differs from the invention defined in claims 1, 8, and 15 in that the combination fails to disclose that the category labels for the QoE metrices are identified from a list of phrases and corresponding categories and attributes of issues. Using a list to link/associated phrases with corresponding categories and attributes is known in the art as evidenced by Komissarichik. Thus, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to modify the QoE metric labeling taught by the combination of Cioffi and Dalton to include a list of phrases (i.e., metrics) and corresponding categories and attributes/context as taught by Komissarichik at it merely constitutes the combination of known elements to achieve the predictable result of labeling metrics.
Although the combination of Cioffi, Dalton, and Komissarichik teaches the server 270 may repair the issue by improving the audio quality and video that is functioning poorly because of poor connectivity, and that an application provider may adjust its audio and video compression schemes to match the user’s available throughput in real time, i.e., during the collaboration session, the combination fails to explicitly teach identifying a media processing adjustment based on the category of the issue as now recited in independent claims 1, 8 and 15.
Ellis teaches a system and method that correlates service issues with system telemetry associated with the software session associated with those service issues that includes, inter alia, identifying a media processing adjustment based on the category of the issue (¶[0073] teaches “a predicted assignment may indicate what type of service issue or what category of service issue this service issue may be. The prediction portion indicate whether the service issue is, for example, a communication service issue, an interface service issue, a storage service issue, etc. …the predicted assignment can indicate whether the service issue should be addressed, deleted, or some other action performed on the service issue”).
The combination of Cioffi, Dalton and Komissarichik differs from the invention defined in claims 1, 8, and 15 in that the combination fails to explicitly disclose that media processing action to be taken is based on the category of the issue. Identifying/selecting actions to be performed based on a service issue category is known in the art as evidenced by Ellis. Thus, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to modify the actions to be taken in response to an issue taught by the combination of Cioffi and Dalton to include using the service issue category to identifying the action to be performed as taught by Ellis at it merely constitutes the combination of known elements to achieve the predictable result of labeling metrics based on categories and utilizing the categories to filter/select corrective actions.
Regarding claims 2, 9, and 16, the combination of Cioffi, Dalton, Komissarichik and Ellis teaches each of the limitations of claims 1, 8, and 15 as discussed above. In addition, Dalton teaches detecting the phrase comprises detecting the phrase using automatic speech recognition (ASR) ([0021] teaches the NLP engine performs speech recognition to detect words in each utterance based on voice audio provided in the mixed media stream).
Cioffi and Dalton are analogous art because they are both directed to improving user experience during an online collaboration/meeting. One having ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to modify the real-time user QoE feedback functionality of Cioffi to include detecting, using ASR, a spoken phrase indicating a triggering event as taught by Dalton in order to offer moderating guidance and/or other timely feedback, thereby enriching the quality of the dialogue between the human participants (Dalton, col. 3, lines 44-46).
Regarding claims 3, 10, and 17, the combination of Cioffi, Dalton, Komissarichik and Ellis teaches each of the limitations of claims 1, 8, and 15 as discussed above. In addition, Cioffi teaches wherein performing the one or more actions comprises: displaying corrective actions to be taken on a display of one of the plurality of users (¶[0348] teaches the user interface (UI) may alert the user to improvement needs and/or prompt the user to start an improvement process. In addition, ¶[0349] teaches the server 270 entity can also prompt a user to start network improvement through an email, app, text, or other alert forms).
Regarding claims 4, 11, and 18, the combination of Cioffi, Dalton, Komissarichik and Ellis teaches each of the limitations of claims 1, 8, and 15 as discussed above. In addition, Cioffi teaches wherein performing the one or more actions comprises: adjusting, at a client of one of the plurality of users, parameters associated with the online collaboration session (Under a broadest reasonable interpretation, the term “client” is interpreted as computer executable functionality and/or instructions. This is supported by ¶ [071] of the instant application which states that a “…client… can be inclusive of an executable file comprising instructions that can be understood and processed on a server, computer, processor, machine, computer node, combinations thereof…”. Cioffi ¶ [0051] teaches the server 270 is able to improve performance by adjusting parameters. In addition, ¶ [0248] teaches different user applications may have difference network usage behavior and application and device prioritizations can assist QoE optimization or improvement and server 270 polices can direct equipment, e.g., at the client, to prioritize based on port number, usage patterns, or IP addresses. Prioritization of client devices based on server polices is interpreted as a corrective action including adjusting parameters at the client).
Regarding claims 5, 12 and 19, the combination of Cioffi, Dalton, Komissarichik and Ellis teaches each of the limitations of claims 1, 8 and 15 as discussed above. In addition, Cioffi teaches estimating that a second issue in a second online collaboration session has occurred without being verbalized, based on parameters associated with the second online collaboration session and information associated with the labeled log of metrics (¶[0073] teaches WFH analytics may estimate QoE from QoS via correlation or relationships learned through artificial intelligence, machine learning, and/or rule-based designer ingenuity/ experience. Such learnings often involve trainings that use actual user QoE reactions (or data), sometimes known in adaptive learning as labels that help create models. Those models then apply to estimate these QoE reactions from future users when these labels are not present. In addition, ¶[0079] teaches direct QoE feedback 360 can help machine learning methods to learn how QoE may be estimated from continuously available QoS data like packet losses, signal levels, noise levels, outages, margin levels, data rates, throughputs, latency (delay), and all other forms of both current and historical operational/performance data); and
logging the second issue (¶[0068] of Cioffi teaches the server 270 includes a metric generation apparatus 288 that generates an aggregated and/or plurality of different metrics applicable to the framework, including QoE metrics. In addition, ¶ [0079] teaches the estimated QoE from QoE estimator 350 replaces the QoE data whenever direct QoE feedback data are not available. Replacing the real-time QoE data with the estimated QoE include logging the data associated with the future/second online collaboration).
Regarding claims 6, 13 and 20, the combination of Cioffi, Dalton, Komissarichik and Ellis teaches each of the limitations of claims 1, 8 and 15 as discussed above. In addition, Cioffi teaches
predicting a second issue in a second online collaboration session based on parameters associated with the second online collaboration session and information associated with the labeled log of metrics (¶[0166] teaches the QoE estimator 350 implements learning methods to estimate QoE from QoS data 375 (operational) and/or 380 (performance). Other types of data may also be employed by the QoE estimator during this estimation process); and
applying corrective actions to prevent the second issue from occurring (¶ [0219] teaches the application provider may adjust its audio and video compression schemes to match the user's available throughput. Certain implementations allow such adjustments and may in fact attempt them in real time, while others may allow historical performance to be appropriately added to the decision process on such dynamic matching of the compression scheme to the expected throughput.)
Allowable Subject Matter
Claims 7 and 14 would be allowable if rewritten to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding dependent claims 7 and 14, the prior art of record fails to disclose or suggest that the attribute includes an indication that a name of the one or more second users was included in the phrase.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PENNY L CAUDLE whose telephone number is (703)756-1432. The examiner can normally be reached M-Th 8:00 am to 5:00 pm eastern.
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/PENNY L CAUDLE/Examiner, Art Unit 2657
/DANIEL C WASHBURN/Supervisory Patent Examiner, Art Unit 2657
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